Age and National Institutes of Health Stroke Scale Score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia: development and external validation of prognostic models
- PMID: 14684776
- DOI: 10.1161/01.STR.0000106761.94985.8B
Age and National Institutes of Health Stroke Scale Score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia: development and external validation of prognostic models
Abstract
Background and purpose: To date, no validated, comprehensive, and practicable model exists to predict functional recovery within the first hours of cerebral ischemic symptoms. The purpose of this study was to externally validate 2 prognostic models predicting functional outcome and survival at 100 days within the first 6 hours after onset of acute cerebral ischemia.
Methods: On admission to a participating hospital, patients were registered prospectively and included according to defined criteria. Follow-up was performed 100 days after the event. With the use of prospectively collected data, 2 prognostic models were developed and internally calibrated in 1079 patients and externally validated in 1307 patients. By means of age and National Institutes of Health Stroke Scale (NIHSS) score as independent variables, model I predicts incomplete functional recovery (Barthel Index <95) versus complete functional recovery, and model II predicts mortality versus survival.
Results: In the validation data set, model I correctly predicted 62.9% of the patients who were incompletely restituted or had died and 83.2% of the completely restituted patients, and model II correctly predicted 57.9% of the patients who had died and 91.5% of the surviving patients. Both models performed better than the treating physicians' predictions made within 6 hours after admission.
Conclusions: The resulting prognostic models are useful to correctly stratify treatment groups in clinical trials and should guide inclusion criteria in clinical trials, which in turn increases the power to detect clinically relevant differences.
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